Democratizing Artificial Intelligence with UiPath. Expand automation in your organization to achieve operational efficiency and high performance

Fanny Ip, Jeremiah Crowley, Tom Torlone

Artificial intelligence (AI) enables enterprises to optimize business processes that are probabilistic, highly variable, and require cognitive abilities with unstructured data. Many believe there is a steep learning curve with AI, however, the goal of our book is to lower the barrier to using AI. This practical guide to AI with UiPath will help RPA developers and tech-savvy business users learn how to incorporate cognitive abilities into business process optimization. With the hands-on approach of this book, you'll quickly be on your way to implementing cognitive automation to solve everyday business problems.Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will help you understand the power of AI and give you an overview of the relevant out-of-the-box models. You’ll learn about cognitive AI in the context of RPA, the basics of machine learning, and how to apply cognitive automation within the development lifecycle. You’ll then put your skills to test by building three use cases with UiPath Document Understanding, UiPath AI Center, and Druid.By the end of this AI book, you'll be able to build UiPath automations with the cognitive capabilities of intelligent document processing, machine learning, and chatbots, while understanding the development lifecycle.

De-Mystifying Math and Stats for Machine Learning. Mastering the Fundamentals of Mathematics and Statistics for Machine Learning

Seaport AI

Beginning with basic concepts like central tendency, dispersion, and types of distribution, this course will help you build a robust understanding of data analysis. It progresses to more advanced topics, including hypothesis testing, outliers, and the intricacies of dependent versus independent variables, ensuring you grasp the statistical tools necessary for data-driven decision-making.Moving ahead, you'll explore the mathematical frameworks crucial for machine learning algorithms. Learn about the significance of percentiles, the distinction between population and sample, and the vital role of precision versus accuracy in data science. Chapters on linear algebra and regression will enhance your ability to implement and interpret complex models, while practical lessons on measuring algorithm accuracy and understanding key machine learning concepts will round out your expertise.The course culminates with an in-depth look at specific machine learning techniques such as decision trees, k-nearest neighbors (kNN), and gradient descent. Each chapter builds on the last, guiding you through a logical progression of knowledge and skills. By the end, you will have not only mastered the theoretical aspects but also gained practical insights into applying these techniques in real-world scenarios.

Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems

Guanhua Wang

Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.

Emotional Intelligence for IT Professionals. The must-have guide for a successful career in IT

Emília M. Ludovino

This book will help you discover your emotional quotient (EQ) through practices and techniques that are used by the most successful IT people in the world. It will make you familiar with the core skills of Emotional Intelligence, such as understanding the role that emotions play in life, especially in the workplace. You will learn to identify the factors that make your behavior consistent, not just to other employees, but to yourself. This includes recognizing, harnessing, predicting, fostering, valuing, soothing, increasing, decreasing, managing, shifting, influencing or turning around emotions and integrating accurate emotional information into decision-making, reasoning, problem solving, etc., because, emotions run business in a way that spreadsheets and logic cannot. When a deadline lurks, you’ll know the steps you need to take to keep calm and composed. You’ll find out how to meet the deadline, and not get bogged down by stress. We’ll explain these factors and techniques through real-life examples faced by IT employees and you’ll learn using the choices that they made. This book will give you a detailed analysis of the events and behavioral pattern of the employees during that time. This will help you improve your own EQ to the extent that you don’t just survive, but thrive in a competitive IT industry.

Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale

Emmanuel Raj

Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you’ll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You’ll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you’ll apply the knowledge you’ve gained to build real-world projects.By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.

Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python

Dipayan Sarkar, Vijayalakshmi Natarajan

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking.The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis.By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.

Essential Mathematics for Quantum Computing. A beginner's guide to just the math you need without needless complexities

Leonard S. Woody III

Quantum computing is an exciting subject that offers hope to solve the world’s most complex problems at a quicker pace. It is being used quite widely in different spheres of technology, including cybersecurity, finance, and many more, but its concepts, such as superposition, are often misunderstood because engineers may not know the math to understand them. This book will teach the requisite math concepts in an intuitive way and connect them to principles in quantum computing.Starting with the most basic of concepts, 2D vectors that are just line segments in space, you'll move on to tackle matrix multiplication using an instinctive method. Linearity is the major theme throughout the book and since quantum mechanics is a linear theory, you'll see how they go hand in hand. As you advance, you'll understand intrinsically what a vector is and how to transform vectors with matrices and operators. You'll also see how complex numbers make their voices heard and understand the probability behind it all.It’s all here, in writing you can understand. This is not a stuffy math book with definitions, axioms, theorems, and so on. This book meets you where you’re at and guides you to where you need to be for quantum computing. Already know some of this stuff? No problem! The book is componentized, so you can learn just the parts you want. And with tons of exercises and their answers, you'll get all the practice you need.

Exploring GPT-3. An unofficial first look at the general-purpose language processing API from OpenAI

Steve Tingiris, Bret Kinsella

Generative Pre-trained Transformer 3 (GPT-3) is a highly advanced language model from OpenAI that can generate written text that is virtually indistinguishable from text written by humans. Whether you have a technical or non-technical background, this book will help you understand and start working with GPT-3 and the OpenAI API.If you want to get hands-on with leveraging artificial intelligence for natural language processing (NLP) tasks, this easy-to-follow book will help you get started. Beginning with a high-level introduction to NLP and GPT-3, the book takes you through practical examples that show how to leverage the OpenAI API and GPT-3 for text generation, classification, and semantic search. You'll explore the capabilities of the OpenAI API and GPT-3 and find out which NLP use cases GPT-3 is best suited for. You’ll also learn how to use the API and optimize requests for the best possible results. With examples focusing on the OpenAI Playground and easy-to-follow JavaScript and Python code samples, the book illustrates the possible applications of GPT-3 in production.By the end of this book, you'll understand the best use cases for GPT-3 and how to integrate the OpenAI API in your applications for a wide array of NLP tasks.